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作 者:甘润东 龙玉江 王策 张焕彬 刘佩 王巍 GAN Rundong;LONG Yujiang;WANG Ce;ZHANG Huanbin;LIU Pei;WANG Wei(Information Center of Guizhou Power Grid Co.,Ltd.,Guiyang 550001,China;Taidou Microelectronics Technology Co.,Ltd.,Guangzhou 510000,China;School of Information Engineering,Wuhan University of Technology,Wuhan 430070,China;School of Electronic Information and Communication,Huazhong University of Science and Technology,Wuhan 430074,China)
机构地区:[1]贵州电网有限责任公司信息中心,贵阳550001 [2]泰斗微电子科技有限公司,广州510000 [3]武汉理工大学信息工程学院,武汉430070 [4]华中科技大学电子信息与通信学院,武汉430074
出 处:《重庆理工大学学报(自然科学)》2023年第12期293-301,共9页Journal of Chongqing University of Technology:Natural Science
基 金:国家自然科学基金项目(62071194);湖北省国际合作项目(2021EHB002)。
摘 要:输电线路常常途径偏远山区,只能采用北斗短报文进行监测信息回传。但是受北斗短报文窄带高误码的信道特性所限,监测图像无法高效稳定地传输。为此,提出一种适应北斗信道特性的高比率压缩和抗误码图像压缩技术,实现高效可靠的输电线路监测。首先设计了自适应感兴趣区域提取算法提取出输电铁塔等关键信息,去除冗余像素;然后,设计了基于注意力机制的图像编/解码器,采用有损数据训练,提升模型的鲁棒性和抗误码性能。实验结果表明:该技术能有效地监测输电铁塔,达到97%的图像压缩率;在误码率为5%的情况下,与变分自动编码器相比,重建图像的峰值信噪比提升了5.2 dB,结构相似度提升了0.21。Transmission lines often pass through remote mountainous areas with limited communications,only BeiDou short message communication(SMC)is available for the transmission of monitoring information.Limited by the narrowband and high BER channel characteristics of BeiDou SMC,the monitoring images are not efficiently or steadily transmitted.For this reason,this paper proposes a high-ratio and BER-resistant image compression system adapted to the channel characteristics of BeiDou SMC to realize efficient and reliable monitoring of transmission lines.First,our system designs an adaptive region of interest extraction algorithm to extract key information such as transmission towers and remove redundant pixels.Then,an image coder/decoder based on attention mechanism is built to train lossy data and improve the robustness and BER resistance of the model.The experimental results show our proposed system effectively monitors transmission towers,achieving 97%image compression,and improving the peak signal-to-noise ratio of the reconstructed image by 5.2 dB and the structural similarity by 0.21 compared with the variational autoencoder when BER stands at 5%.
分 类 号:TM75[电气工程—电力系统及自动化]
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